Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection
Abstract
We consider the dynamic classifier selection (DCS) problem: Given an ensemble of classifiers, we are to choose which classifier to use depending on the particular input vector that we get to classify. The problem is a special case of the general algorithm selection problem where we have multiple different algorithms we can employ to process a given input. We investigate if a method developed for general algorithm selection named cost-sensitive hierarchical clustering (CSHC) is suited for DCS. We introduce some additions to the original CSHC method for the special case of choosing a classification algorithm and evaluate their impact on performance. We then compare with a number of state-of-the-art dynamic classifier selection methods. Our experimental results show that our modified CSHC algorithm compares favorably
Cite
@article{arxiv.2012.09608,
title = {Cost-sensitive Hierarchical Clustering for Dynamic Classifier Selection},
author = {Meinolf Sellmann and Tapan Shah},
journal= {arXiv preprint arXiv:2012.09608},
year = {2020}
}